Visualization with ggplot2 Part 2
library(tidyverse)## ── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(patchwork)Load the weather dataset
weather_df =
rnoaa::meteo_pull_monitors(
c("USW00094728", "USC00519397", "USS0023B17S"),
var = c("PRCP", "TMIN", "TMAX"),
date_min = "2017-01-01",
date_max = "2017-12-31") %>%
mutate(
name = recode(
id,
USW00094728 = "CentralPark_NY",
USC00519397 = "Waikiki_HA",
USS0023B17S = "Waterhole_WA"),
tmin = tmin / 10,
tmax = tmax / 10) %>%
select(name, id, everything())## Registered S3 method overwritten by 'hoardr':
## method from
## print.cache_info httr## using cached file: /Users/yiming/Library/Caches/R/noaa_ghcnd/USW00094728.dly## date created (size, mb): 2020-10-07 16:20:31 (7.525)## file min/max dates: 1869-01-01 / 2020-10-31## using cached file: /Users/yiming/Library/Caches/R/noaa_ghcnd/USC00519397.dly## date created (size, mb): 2020-10-07 16:20:49 (1.699)## file min/max dates: 1965-01-01 / 2020-03-31## using cached file: /Users/yiming/Library/Caches/R/noaa_ghcnd/USS0023B17S.dly## date created (size, mb): 2020-10-07 16:21:03 (0.88)## file min/max dates: 1999-09-01 / 2020-10-31weather_df## # A tibble: 1,095 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2017-01-01 0 8.9 4.4
## 2 CentralPark_NY USW00094728 2017-01-02 53 5 2.8
## 3 CentralPark_NY USW00094728 2017-01-03 147 6.1 3.9
## 4 CentralPark_NY USW00094728 2017-01-04 0 11.1 1.1
## 5 CentralPark_NY USW00094728 2017-01-05 0 1.1 -2.7
## 6 CentralPark_NY USW00094728 2017-01-06 13 0.6 -3.8
## 7 CentralPark_NY USW00094728 2017-01-07 81 -3.2 -6.6
## 8 CentralPark_NY USW00094728 2017-01-08 0 -3.8 -8.8
## 9 CentralPark_NY USW00094728 2017-01-09 0 -4.9 -9.9
## 10 CentralPark_NY USW00094728 2017-01-10 0 7.8 -6
## # … with 1,085 more rowsRemeber this plot..?
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .5)## Warning: Removed 15 rows containing missing values (geom_point).
Labels
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .5) +
labs(
tittle = "Tempperature plot",
x = "Minimum daily temperature(C)",
y = "Maximum daily temperature(C)",
caption = "Data from rnoaa package; tempereature in 2017."
)## Warning: Removed 15 rows containing missing values (geom_point).
Scales
Start with the same plot; x and y scales
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .5) +
labs(
tittle = "Tempperature plot",
x = "Minimum daily temperature(C)",
y = "Maximum daily temperature(C)",
caption = "Data from rnoaa package; tempereature in 2017."
) +
scale_x_continuous(
breaks = c(-15, 0 ,15),
labels = c("-15 C", "0", "15")
)+
scale_y_continuous(
position = "right",
trans = "log"
)## Warning in self$trans$transform(x): NaNs produced## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 90 rows containing missing values (geom_point).
Look at color scales
#通过使用scale_color_hue function 可以改变variable color范围，可以改变categorical name by using argument "name" in scale_color_hue function(name = "xxxx")
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .5) +
labs(
tittle = "Tempperature plot",
x = "Minimum daily temperature(C)",
y = "Maximum daily temperature(C)",
caption = "Data from rnoaa package; tempereature in 2017."
) +
scale_color_hue(
name = "Location",
h = c(100, 300))## Warning: Removed 15 rows containing missing values (geom_point).
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .5) +
labs(
tittle = "Tempperature plot",
x = "Minimum daily temperature(C)",
y = "Maximum daily temperature(C)",
caption = "Data from rnoaa package; tempereature in 2017."
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE)## Warning: Removed 15 rows containing missing values (geom_point).
Themes
Shift the legend
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .5) +
labs(
tittle = "Tempperature plot",
x = "Minimum daily temperature(C)",
y = "Maximum daily temperature(C)",
caption = "Data from rnoaa package; tempereature in 2017."
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE) +
theme(
legend.position = "bottom"
)## Warning: Removed 15 rows containing missing values (geom_point).
Change the overall theme.
#theme(legend.position)必须在theme_bw这类改plot背景的code之后
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .5) +
labs(
tittle = "Tempperature plot",
x = "Minimum daily temperature(C)",
y = "Maximum daily temperature(C)",
caption = "Data from rnoaa package; tempereature in 2017."
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE) +
theme_bw()+
theme(
legend.position = "bottom"
)## Warning: Removed 15 rows containing missing values (geom_point).
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .5) +
labs(
tittle = "Tempperature plot",
x = "Minimum daily temperature(C)",
y = "Maximum daily temperature(C)",
caption = "Data from rnoaa package; tempereature in 2017."
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE) +
ggthemes::theme_excel()## Warning: Removed 15 rows containing missing values (geom_point).
Setting options
放在每个Rmarkdown文件最开始的设定如下
library(tidyverse)
knitr::opts_chunk$set(
fig.width = 6,
fig.asp = .6,
out.width = "90%"
)
theme_set(theme_minimal() + theme(legend.position = "bottom"))
options(
ggplot2.continuous.colour = "viridis",
ggplot2.continuous.fill = "viridis"
)
scale_colour_discrete = scale_colour_viridis_d
scale_fill_discrete = scale_fill_viridis_dDate args in “geom”
central_park =
weather_df %>%
filter(name == "CentralPark_NY")
waikiki =
weather_df %>%
filter(name == "Waikiki_HA")
## 如何让同一个图里出现一个是scatteplot一个是现状图
ggplot(data = waikiki, aes(x = date, y =tmax, color = name)) +
geom_point() +
geom_line(data = central_park)## Warning: Removed 3 rows containing missing values (geom_point).
patchwork
remeber facting?
weather_df %>%
ggplot(aes(x = tmin, fill = name)) +
geom_density(alpha = .5) +
facet_grid(. ~name)## Warning: Removed 15 rows containing non-finite values (stat_density).
What happens when you want multipanel plots but can’t facet..?
##再不能用facet的情况下，混合multipanel到一个页面的方法
tmax_tmin_p =
weather_df %>%
ggplot(aes(x = tmin, y =tmax, color = name)) +
geom_point(alpha = .5) +
theme(legend.position = "none")
prcp_dens_p =
weather_df %>%
filter(prcp >0) %>%
ggplot(aes(x = prcp, fill = name)) +
geom_density(alpha = .5)
tmax_date_p =
weather_df %>%
ggplot(aes(x = date, y = tmax, color = name)) +
geom_point() +
geom_smooth(se = FALSE) +
theme(legend.position = "none")
#三个图并列排
tmax_tmin_p + prcp_dens_p + tmax_date_p## Warning: Removed 15 rows containing missing values (geom_point).## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).
#第一个图在最上面，后两个在第二层并列
tmax_tmin_p /(prcp_dens_p + tmax_date_p)## Warning: Removed 15 rows containing missing values (geom_point).## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).
Data manipulation
Control your factors.
#因为r的逻辑是把categorical varianles转换成factor123，然后在作图的时候又识别123分别是什么以categorical name显示
#所以想改变同一个图的panel顺序，比如把xxx放在第一个，那就是要进行data manipulation，而不是在ggplot进行操作
#下面这个步骤就是用fct_relevel把waikiki panel提到第一个
#homework3 prob1跟这个题有点类似，原本图x-axis是根据aisle alpha name排列的， 但是我想让它根据n从小到大排列：aisle = fct_reorder(aisle, n)
weather_df %>%
mutate(
name = factor(name),
name= forcats::fct_relevel(name, c("Waikiki_HA"))
) %>%
ggplot(aes(x = name,y = tmax, fill = name)) +
geom_violin(alpha = .5)## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
What is I wanted densities for tmin and tmax simultaneously?(就是tmin 和tmax density plot overlaid)
weather_df %>%
filter(name == "CentralPark_NY") %>%
pivot_longer(
tmax:tmin,
names_to = "observation",
values_to = "temperature"
) %>%
ggplot(aes(x = temperature, fill = observation)) +
geom_density(alpha = .5) 
weather_df %>%
pivot_longer(
tmax:tmin,
names_to = "observation",
values_to = "temperature"
) %>%
ggplot(aes(x = temperature, fill = observation)) +
geom_density(alpha = .5) +
facet_grid(. ~ name)## Warning: Removed 18 rows containing non-finite values (stat_density).
Revisit the pups
Date from the FAS study
pup_data =
read_csv("./data/FAS_pups.csv") %>%
janitor::clean_names() %>%
mutate(sex = recode(sex, `1` = "male", `2` = "female"))## Parsed with column specification:
## cols(
## `Litter Number` = col_character(),
## Sex = col_double(),
## `PD ears` = col_double(),
## `PD eyes` = col_double(),
## `PD pivot` = col_double(),
## `PD walk` = col_double()
## )litters_data =
read_csv("./data/FAS_litters.csv") %>%
janitor::clean_names() %>%
separate(group, into = c("dose", "day_of_tx"), sep = 3)## Parsed with column specification:
## cols(
## Group = col_character(),
## `Litter Number` = col_character(),
## `GD0 weight` = col_double(),
## `GD18 weight` = col_double(),
## `GD of Birth` = col_double(),
## `Pups born alive` = col_double(),
## `Pups dead @ birth` = col_double(),
## `Pups survive` = col_double()
## )fas_data = left_join(pup_data, litters_data, by = "litter_number")
fas_data %>%
select(dose, day_of_tx, starts_with("pd")) %>%
pivot_longer(
pd_ears:pd_walk,
names_to = "outcome",
values_to = "pn_day"
) %>%
drop_na() %>%
mutate(outcome = forcats::fct_relevel(outcome,"pd_ears", "pd_pivot","pd_walk", "pd_eyes")) %>%
ggplot(aes(x = dose, y = pn_day)) +
geom_violin() +
facet_grid(day_of_tx ~ outcome)